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          AutoTVM 探秘 （三）
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        <p>对于目前的优化算法来说，依然存在着许多问题。但是后续的工作并不是特别多。首先可以看一下 <a target="_blank" rel="noopener" href="https://arxiv.org/abs/1905.12799">Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation, ICML 2019 Workshop RL4RealLife</a> 这篇文章主要谈到的问题是两点：1. 开发一个更有效的搜索算法（相对于 AutoTVM 的模拟退火） 2. 减少硬件测试的时间。从这篇文章的实验结果来看，第一个目标基本上没有达成，第二个目标完成的还不错。这个第二个目标也是我认为的之后优化的核心问题。 文章的整个框架如下图，主要贡献是两个蓝色的部分——基于强化学习的搜索和自适应采样。</p>
<span id="more"></span>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-上午11.50.38.png"></p>
<p>整个过程不是很复杂，这个强化学习其实就是用来代替模拟退火的。迭代数次 Policy Network，输入是一个当前算子的 config，也就是之前说的 schedule，输出是对 config 的上下调整。强化学习要从环境里面获取代价，这个代价其实就是从 cost model 里面预测出来的每个 config 的运行时间，再用 PPO 的方式去训练 Policy Network，这个强化学习套路感觉非常的强行，最后效果也一般般。 然后对所有的 config 进行自适应采样，只对采样出来的 config 在硬件上测试实际的运行时间，然后将采样出来的 config 用于 cost model 的训练。这个所谓的自适应采样其实非常简单，就是对所有 config 做一个 k-means，然后采样每个centroid。</p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午12.01.27.png"></p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午12.01.34.png"></p>
<p>从上面两个图可以看出，第二步自适应采样的动机还是比较强的。因为对于 AutoTVM 来说，大量的时间都用于在硬件上测试算子的运行时间，而且相似的算子 config 确实很多，所以通过聚类然后采样的想法确实比较直接。最后的加速效果也不错：</p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午12.04.10.png"></p>
<p>通过结果可以看出来，虽然最后的加速效果很不错，但是对于结果的优化程度几乎没什么变化。说明第二步自适应采样很有效，但是第一步强化学习其实没什么用。讽刺的是这个进的还是 RL4RealLife Workshop... 对于 AutoTVM 来说，目前最主要的问题还是 tuning 的时间过慢。所以 AutoTVM 只能用于 inference，不能用于 training。因为你 tuning 的时间很有可能就比 training 的时间长了...</p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午12.07.50.png"></p>
<p>从上面的图中可以看到，tuning 一次 MobileNet，在 V100 上面都要花差不多 19 个小时，非常缓慢。在我们实验室这种显卡上面，大概就要两到三天了。</p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午4.16.54.png"></p>
<p>在 2019 年 12 月 5 日结束的<a target="_blank" rel="noopener" href="https://sampl.cs.washington.edu/tvmconf/">第二届TVM与深度学习编译器会议</a>上面，也有一个思路类似的 talk。是来自 AWS 的 <a target="_blank" rel="noopener" href="https://sampl.cs.washington.edu/tvmconf/slides/2019/E07-Cody-Yu.pdf">Improving AutoTVM Efficiency by Schedule Sharing</a>。跟上面的那篇文章非常类似，也是用聚类去优化 AutoTVM。 从上面的图中可以看出，对于每个从模型中抽取的 task，都要进行 turning。这个工作的动机是，如果一个 schedule 在一个 conv2d 上面效果良好，那他在另一个 conv2d 上面的效果应该也还不错。这意味着可以利用一些有代表性的任务来 turning，然后把该任务的 schedule 直接迁移到相似的任务上面去。这里的距离计算方式是 turning space 的重叠比率，然后利用这个距离来聚类。</p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午3.46.39.png"></p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午3.46.54.png"></p>
<p>从上面这个图中可以看出，Schedule Sharing 可以在平均 28% 的调整时间里获得 84% 的加速效果。相关讨论和 PR 在 <a target="_blank" rel="noopener" href="https://github.com/apache/incubator-tvm/issues/4188">github issue</a> 上面可以找到。 新的搜索方法，还有一篇 <a target="_blank" rel="noopener" href="https://arxiv.org/abs/1909.10616">Compiler-Level Matrix Multiplication Optimization for Deep Learning, arXiv</a>。这篇文章只把问题限制在了调 MM 的 tile size 上面，整个搜索空间与问题范围缩小了很多，而且去掉了 cost model，直接用强化学习来指导搜索，其实就相当于有一个很慢但是很准确的 cost model，跟前面讲的第一篇非常相似。（虽然这个 cost model 可能比 XGB 慢了差不多 1000 倍吧 XD） 总结一下上面的几个工作，几乎都是采取了一些很简单的做法，就对 AutoTVM 的整个 turning 时间起到了巨大的提升。说明这方面研究的潜力还是非常大的，如果能压缩到五分钟 turning 完一个网络，说不定就可以用 AutoTVM 来帮助 training。（当然现在来看还都是空谈，因为最好的工作也就是四到五倍的压缩效率，从两天变成半天）</p>
<p>说完了对搜索方法和训练采样方法的一些魔改方法之后，下面应该要说一些对于 AutoTVM 的核心 cost model 的魔改方法了。 目前对于 cost model 的研究集中于 GNN 上面，<a target="_blank" rel="noopener" href="https://sampl.cs.washington.edu/tvmconf/">第二届TVM与深度学习编译器会议</a>上面有一个 UW 的 Talk，题目是 <a target="_blank" rel="noopener" href="https://sampl.cs.washington.edu/tvmconf/slides/2019/E01-Eddie-Yan.pdf">Graph Convolutional Cost Models for TVM</a> ，还有一篇 <a target="_blank" rel="noopener" href="https://arxiv.org/abs/1904.11876">Simulating Execution Time of Tensor Programs using Graph Neural Networks，ICLR 2019 workshop at Representation Learning on Graphs and Manifolds</a>。这两个工作基本是一样的，都是用 GCN 去优化 cost model。然而怪异的是两个工作的结果都是跟 XGB 在一个类似于估计运行时间的数据集上面的对比，没有最后 end-to-end 的效果提升。 两个工作都是用 AST 建图，大概长这样：</p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午4.19.55.png"></p>
<p>然后用 GCN 求一下 embedding，然后把所有 embedding 都平均一下，然后接个 MLP...</p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午4.33.14.png"></p>
<p>然后没了。</p>
<p><img src="/2020/01/02/autotvm-3/屏幕快照-2020-01-02-下午4.35.55.png"></p>
<p>这效果看起来其实也就那样，而且还没测 end-to-end 的性能，估计是实在不能看。</p>

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